Deep Learning-Based Urban Pluvial Flood Modeling using High-resolution Physical Information

As climate change intensifies hydrologic extremes, the need for near real-time urban flood prediction becomes critical. Pluvial flooding occurs when intense rainfall overwhelms urban drainage systems, involving complex hydrodynamic interactions between surface runoff and subsurface sewer flow—known as dual drainage. Capturing the spatiotemporal evolution of these processes requires detailed representations of flow patterns, inundation propagation, and runoff accumulation. However, physics-based hydrodynamic models, while effective at resolving the fine-scale dynamics of flood events, face significant computational limitations, particularly for large urban areas or high-resolution domains. To address this challenge, we propose a deep learning-based urban flood prediction model that integrates surface runoff dynamics with sewer network interactions. The model is developed using training data generated from physics-based 1D-2D hydrodynamic simulations that capture interactions between 2D surface flow and 1D sewer network flow. The Oncheoncheon River catchment in Busan, South Korea—a region frequently impacted by urban flooding—serves as the study area. Various synthetic rainfall scenarios are used to train the model, ensuring its ability to generalize across different extreme rainfall events. Model validation against historical flood events shows that the deep learning model accurately predicts flood evolution patterns while significantly reducing computational time compared to traditional hydrodynamic models. This study demonstrates the potential of deep learning-based approaches to enhance real-time urban flood prediction and provides valuable insights for developing efficient, data-driven disaster management strategies.